[WIP] Unify tests (#214)

* Converge test fixtures Signed-off-by: gaugup <gaugup@microsoft.com> * Unify tests Signed-off-by: gaugup <gaugup@microsoft.com> * Unify more tests Signed-off-by: gaugup <gaugup@microsoft.com> * Fix lint Signed-off-by: gaugup <gaugup@microsoft.com> * Migrate more tests Signed-off-by: gaugup <gaugup@microsoft.com> * Migrate few more tests to common tests Signed-off-by: gaugup <gaugup@microsoft.com> * Unify more tests Signed-off-by: gaugup <gaugup@microsoft.com>
interpretml · Sep 14, 2021 · 74c3f8b · 74c3f8b
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Showing 5 changed files with 288 additions and 254 deletions.
diff --git a/tests/conftest.py b/tests/conftest.py
@@ -52,6 +52,34 @@ def regression_exp_object(method="random"):
     return exp
 
 
+@pytest.fixture(scope='session')
+def custom_public_data_interface():
+    dataset = helpers.load_custom_testing_dataset_regression()
+    d = dice_ml.Data(dataframe=dataset, continuous_features=['Numerical'], outcome_name='Outcome')
+    return d
+
+
+@pytest.fixture(scope='session')
+def sklearn_binary_classification_model_interface():
+    ML_modelpath = helpers.get_custom_dataset_modelpath_pipeline_binary()
+    m = dice_ml.Model(model_path=ML_modelpath, backend='sklearn', model_type='classifier')
+    return m
+
+
+@pytest.fixture(scope='session')
+def sklearn_multiclass_classification_model_interface():
+    ML_modelpath = helpers.get_custom_dataset_modelpath_pipeline_multiclass()
+    m = dice_ml.Model(model_path=ML_modelpath, backend='sklearn', model_type='classifier')
+    return m
+
+
+@pytest.fixture(scope='session')
+def sklearn_regression_model_interface():
+    ML_modelpath = helpers.get_custom_dataset_modelpath_pipeline_regression()
+    m = dice_ml.Model(model_path=ML_modelpath, backend='sklearn', model_type='regression')
+    return m
+
+
 @pytest.fixture
 def public_data_object():
     """

diff --git a/tests/test_dice_interface/test_dice_KD.py b/tests/test_dice_interface/test_dice_KD.py
@@ -2,7 +2,6 @@
 import numpy as np
 import dice_ml
 from dice_ml.utils import helpers
-from dice_ml.utils.exception import UserConfigValidationException
 from dice_ml.diverse_counterfactuals import CounterfactualExamples
 from dice_ml.counterfactual_explanations import CounterfactualExplanations
 
@@ -46,17 +45,6 @@ def _initiate_exp_object(self, KD_binary_classification_exp_object):
         self.exp = KD_binary_classification_exp_object  # explainer object
         self.data_df_copy = self.exp.data_interface.data_df.copy()
 
-    # When no elements in the desired_class are present in the training data
-    @pytest.mark.parametrize("desired_class, total_CFs", [(1, 3), ('a', 3)])
-    def test_unsupported_binary_class(self, desired_class, sample_custom_query_1, total_CFs):
-        with pytest.raises(UserConfigValidationException) as ucve:
-            self.exp._generate_counterfactuals(query_instance=sample_custom_query_1, total_CFs=total_CFs,
-                                               desired_class=desired_class)
-        if desired_class == 1:
-            assert "Desired class not present in training data!" in str(ucve)
-        else:
-            assert "The target class for {0} could not be identified".format(desired_class) in str(ucve)
-
     # When a query's feature value is not within the permitted range and the feature is not allowed to vary
     @pytest.mark.parametrize("desired_range, desired_class, total_CFs, features_to_vary, permitted_range",
                              [(None, 0, 4, ['Numerical'], {'Categorical': ['b', 'c']})])
@@ -119,20 +107,6 @@ def test_permitted_range_categorical(self, desired_class, sample_custom_query_2,
                                            total_CFs=total_CFs, permitted_range=permitted_range)
         assert all(i in permitted_range["Categorical"] for i in self.exp.final_cfs_df.Categorical.values)
 
-    # Testing if an error is thrown when the query instance has an unknown categorical variable
-    @pytest.mark.parametrize("desired_class, total_CFs", [(0, 1)])
-    def test_query_instance_outside_bounds(self, desired_class, sample_custom_query_3, total_CFs):
-        with pytest.raises(ValueError):
-            self.exp._generate_counterfactuals(query_instance=sample_custom_query_3, total_CFs=total_CFs,
-                                               desired_class=desired_class)
-
-    # Testing if an error is thrown when the query instance has an unknown column
-    @pytest.mark.parametrize("desired_class, total_CFs", [(0, 1)])
-    def test_query_instance_unknown_column(self, desired_class, sample_custom_query_5, total_CFs):
-        with pytest.raises(ValueError):
-            self.exp._generate_counterfactuals(query_instance=sample_custom_query_5, total_CFs=total_CFs,
-                                               desired_class=desired_class)
-
     # Ensuring that there are no duplicates in the resulting counterfactuals even if the dataset has duplicates
     @pytest.mark.parametrize("desired_class, total_CFs", [(0, 2)])
     def test_duplicates(self, desired_class, sample_custom_query_4, total_CFs):
@@ -147,12 +121,6 @@ def test_duplicates(self, desired_class, sample_custom_query_4, total_CFs):
 
         assert all(self.exp.final_cfs_df == expected_output)
 
-    # Testing for 0 CFs needed
-    @pytest.mark.parametrize("desired_class, total_CFs", [(0, 0)])
-    def test_zero_cfs(self, desired_class, sample_custom_query_4, total_CFs):
-        self.exp._generate_counterfactuals(query_instance=sample_custom_query_4, total_CFs=total_CFs,
-                                           desired_class=desired_class)
-
     # Testing for index returned
     @pytest.mark.parametrize("desired_class, total_CFs", [(0, 1)])
     @pytest.mark.parametrize('posthoc_sparsity_algorithm', ['linear', 'binary', None])
@@ -179,33 +147,6 @@ def test_KD_tree_output(self, desired_class, sample_custom_query_2, total_CFs,
                                                  posthoc_sparsity_algorithm=posthoc_sparsity_algorithm)
         assert all(i == desired_class for i in self.exp_multi.cfs_preds)
 
-    # Testing that the output of multiclass classification lies in the desired_class
-    @pytest.mark.parametrize("desired_class, total_CFs", [(2, 3)])
-    def test_KD_tree_counterfactual_explanations_output(self, desired_class, sample_custom_query_2, total_CFs):
-        counterfactual_explanations = self.exp_multi.generate_counterfactuals(
-                                        query_instances=sample_custom_query_2, total_CFs=total_CFs,
-                                        desired_class=desired_class)
-        assert all(i == desired_class for i in self.exp_multi.cfs_preds)
-
-        assert counterfactual_explanations is not None
-
-    # Testing for 0 CFs needed
-    @pytest.mark.parametrize("desired_class, total_CFs", [(0, 0)])
-    def test_zero_cfs(self, desired_class, sample_custom_query_4, total_CFs):
-        self.exp_multi._generate_counterfactuals(query_instance=sample_custom_query_4, total_CFs=total_CFs,
-                                                 desired_class=desired_class)
-
-    # When no elements in the desired_class are present in the training data
-    @pytest.mark.parametrize("desired_class, total_CFs", [(100, 3), ('opposite', 3)])
-    def test_unsupported_multiclass(self, desired_class, sample_custom_query_4, total_CFs):
-        with pytest.raises(UserConfigValidationException) as ucve:
-            self.exp_multi._generate_counterfactuals(query_instance=sample_custom_query_4, total_CFs=total_CFs,
-                                                     desired_class=desired_class)
-        if desired_class == 100:
-            assert "Desired class not present in training data!" in str(ucve)
-        else:
-            assert "Desired class cannot be opposite if the number of classes is more than 2." in str(ucve)
-
 
 class TestDiceKDRegressionMethods:
     @pytest.fixture(autouse=True)

diff --git a/tests/test_dice_interface/test_dice_genetic.py b/tests/test_dice_interface/test_dice_genetic.py
@@ -56,18 +56,6 @@ def test_invalid_query_instance(self, sample_custom_query_1, features_to_vary, p
         with pytest.raises(ValueError):
             self.exp.setup(features_to_vary, permitted_range, sample_custom_query_1, feature_weights)
 
-    # # Testing that the counterfactuals are in the desired class
-    @pytest.mark.parametrize("desired_class, total_CFs, features_to_vary, initialization",
-                             [(1, 2, "all", "kdtree"), (1, 2, "all", "random")])
-    def test_desired_class(self, desired_class, sample_custom_query_2, total_CFs, features_to_vary, initialization):
-        ans = self.exp.generate_counterfactuals(query_instances=sample_custom_query_2,
-                                                features_to_vary=features_to_vary,
-                                                total_CFs=total_CFs, desired_class=desired_class,
-                                                initialization=initialization)
-        for cfs_example in ans.cf_examples_list:
-            assert all(
-                cfs_example.final_cfs_df[self.exp.data_interface.outcome_name].values == [desired_class] * total_CFs)
-
     # Testing that the features_to_vary argument actually varies only the features that you wish to vary
     @pytest.mark.parametrize("desired_class, total_CFs, features_to_vary, initialization",
                              [(1, 2, ["Numerical"], "kdtree"), (1, 2, ["Numerical"], "random")])
@@ -121,18 +109,6 @@ def test_permitted_range_categorical(self, desired_class, total_CFs, features_to
                     permitted_range[feature][1] for i
                     in range(total_CFs))
 
-    # Testing if an error is thrown when the query instance has an unknown categorical variable
-    @pytest.mark.parametrize("desired_class, total_CFs, features_to_vary", [(0, 1, "all")])
-    def test_query_instance_outside_bounds(self, desired_class, sample_custom_query_3, total_CFs, features_to_vary):
-        with pytest.raises(ValueError):
-            self.exp.setup(features_to_vary, None, sample_custom_query_3, "inverse_mad")
-
-    # Testing if an error is thrown when the query instance has an unknown categorical variable
-    @pytest.mark.parametrize("features_to_vary", [("all")])
-    def test_query_instance_unknown_column(self, sample_custom_query_5, features_to_vary):
-        with pytest.raises(ValueError):
-            self.exp.setup(features_to_vary, None, sample_custom_query_5, "inverse_mad")
-
     # Testing if an error is thrown when the query instance has outcome variable
     def test_query_instance_with_target_column(self, sample_custom_query_6):
         with pytest.raises(ValueError) as ve:
@@ -167,16 +143,6 @@ class TestDiceGeneticMultiClassificationMethods:
     def _initiate_exp_object(self, genetic_multi_classification_exp_object):
         self.exp = genetic_multi_classification_exp_object  # explainer object
 
-    # Testing that the counterfactuals are in the desired class
-    @pytest.mark.parametrize("desired_class, total_CFs, initialization", [(2, 2, "kdtree"), (2, 2, "random")])
-    def test_desired_class(self, desired_class, sample_custom_query_2, total_CFs, initialization):
-        ans = self.exp.generate_counterfactuals(query_instances=sample_custom_query_2,
-                                                total_CFs=total_CFs, desired_class=desired_class,
-                                                initialization=initialization)
-        for cfs_example in ans.cf_examples_list:
-            assert all(
-                cfs_example.final_cfs_df[self.exp.data_interface.outcome_name].values == [desired_class] * total_CFs)
-
     # Testing if only valid cfs are found after maxiterations
     @pytest.mark.parametrize("desired_class, total_CFs, initialization, maxiterations",
                              [(2, 7, "kdtree", 0), (2, 7, "random", 0)])